17  CJS model

Simple CJS models to get phi and p estimates to compare with Neural Network CMR

In all the models below, 1 = not observed and 2 = observed in the input encounter data.
Encounter data are available here in the eh.csv file.

17.1 [t,t] model

Model with phi and p for each age-in-samples (column in the encounter history file)

Probability of survival (phi) model structure:

phi[t] <- betaInt[t]

Probability of capture (p) model structure:

p[t] <- betaP[t]

Model code is in ./models/cmrNN_OB/tt/modelCMR_tt_NN_OB_functionsToSource.R
Model is run ‘by hand’ in ./models/cmrNN_OB/tt/modelCMR_tt_NN_OB_makeFile.R
Not using this: Targets are loaded in R/modelCMR_tt_NN_OB.R and saved as modelCMR_tt_NN_OB_target

Model runs:
tt_NN_OB_2023-06-14 23.RData: preliminary run only tracing phi and p
tt_NN_OB_2023-06-15 08.RData: tracing phi and p and z [most recent]

17.1.1 Retrieve model results

Code

# Following https://oliviergimenez.github.io/bayesian-cr-workshop/worksheets/4_demo.html
# 
  load('./models/cmrNN_OB/tt/runsOut/tt_NN_OB_mostRecent.RData')
  out_NN_OB <- d
  
  #Input data
  eh <- tar_read(eh_OB_2002_2014_target)

17.1.2 Model code

In the model code, a value of 1 for z or in gamma or omega signifies the individual is alive and a value of 2 signifies the individual is dead. y[i,j] is the observed data (encounter history file).

Code

  out_NN_OB$modelCode

{
    delta[1] <- 1
    delta[2] <- 0
    for (t in 1:(T - 1)) {
        phi[t] ~ dunif(0, 1)
        gamma[1, 1, t] <- phi[t]
        gamma[1, 2, t] <- 1 - phi[t]
        gamma[2, 1, t] <- 0
        gamma[2, 2, t] <- 1
        p[t] ~ dunif(0, 1)
        omega[1, 1, t] <- 1 - p[t]
        omega[1, 2, t] <- p[t]
        omega[2, 1, t] <- 1
        omega[2, 2, t] <- 0
    }
    for (i in 1:N) {
        z[i, first[i]] ~ dcat(delta[1:2])
        for (j in first[i]:(last[i] - 1)) {
            z[i, j + 1] ~ dcat(gamma[z[i, j], 1:2, j])
            y[i, j + 1] ~ dcat(omega[z[i, j + 1], 1:2, j])
        }
    }
}

17.1.3 Model statistics

Run statistics

nIter	nBurnin	nChains	thinRate
5000	2000	2	5

[1] “Run time = 33.374 mins”

17.1.4 Plot traces

Code

  priors <- runif(out_NN_OB$runData$nIter * out_NN_OB$runData$nChains, 0, 1)
  MCMCtrace(object = out_NN_OB$mcmc,
            #ISB = FALSE,
            #exact = TRUE, 
            params = c("phi", "p"),
            pdf = FALSE, 
            priors = priors)

17.1.5 Summary data

Code

  MCMCplot(object = out_NN_OB$mcmc, params = c("phi"))

Code

  MCMCplot(object = out_NN_OB$mcmc, params = c("p"))

Summary values

Code

#|
  (summary_tt <- MCMCsummary(object = out_NN_OB$mcmc, params = c("phi", "p"), round = 3) %>%
    rownames_to_column(var = "var"))

       var  mean    sd  2.5%   50% 97.5% Rhat n.eff
1   phi[1] 0.812 0.022 0.769 0.812 0.854 1.01   290
2   phi[2] 0.810 0.025 0.762 0.811 0.859 1.00   218
3   phi[3] 0.749 0.021 0.708 0.749 0.792 1.01   230
4   phi[4] 0.647 0.021 0.606 0.647 0.689 1.00   361
5   phi[5] 0.640 0.026 0.587 0.640 0.687 1.00   258
6   phi[6] 0.691 0.035 0.629 0.690 0.764 1.05   206
7   phi[7] 0.830 0.051 0.736 0.827 0.932 1.02   100
8   phi[8] 0.330 0.032 0.271 0.330 0.393 1.00   312
9   phi[9] 0.669 0.079 0.528 0.665 0.837 1.01   200
10 phi[10] 0.560 0.090 0.401 0.556 0.754 1.08   188
11 phi[11] 0.684 0.151 0.412 0.679 0.958 1.00   103
12    p[1] 0.597 0.022 0.555 0.597 0.638 1.00   449
13    p[2] 0.448 0.019 0.410 0.447 0.487 1.01   512
14    p[3] 0.707 0.020 0.669 0.707 0.746 1.00   441
15    p[4] 0.659 0.022 0.615 0.659 0.701 1.01   452
16    p[5] 0.634 0.025 0.587 0.634 0.682 1.01   457
17    p[6] 0.499 0.028 0.447 0.500 0.554 1.01   380
18    p[7] 0.701 0.041 0.616 0.701 0.780 1.00   156
19    p[8] 0.783 0.048 0.682 0.788 0.869 1.00   401
20    p[9] 0.605 0.072 0.458 0.608 0.736 1.03   325
21   p[10] 0.692 0.088 0.512 0.695 0.849 1.03   312
22   p[11] 0.733 0.155 0.438 0.727 0.989 1.00   123

Code

  # To get the mMCMCSummaryRMNA funcion which I adapted to deal with NAs
  source('./models/cmrNN_OB/tt/modelCMR_tt_NN_OB_functionsToSource.R')
  
  summary_tt_z0 <- MCMCSummaryRMNA(object = out_NN_OB$mcmc, params = c("z"), round = 3) %>%
    rownames_to_column(var = "var") |> 
    mutate(
      ind0 = str_match(var, "([0-9]+), ([0-9]+)")[,2],
      t0 =   str_match(var, "([0-9]+), ([0-9]+)")[,3],
      ind = as.numeric(ind0),
      t = as.numeric(t0)
    ) |> 
    dplyr::select(-c(t0, ind0))

Add other variables to summary values

Code

  ehLong <- 
    eh$eh |>
    as.data.frame() |> 
    rownames_to_column("ind0") |> 
    pivot_longer(starts_with("ais")) |> 
    mutate(
      t = as.numeric(str_match(name, "([0-9]+)")[,1]),
      enc = value,
      ind = as.numeric(ind0)
    ) |> 
    dplyr::select(-c(name, value, ind0))
  
  firstLong <- eh$first |> 
    as_tibble() |> 
    rownames_to_column("ind") |> 
    mutate(ind = as.numeric(ind)) |> 
    rename(first = value)
  
  lastLong <- eh$last |> 
    as_tibble() |> 
    rownames_to_column("ind") |> 
    mutate(ind = as.numeric(ind)) |> 
    rename(last = value)
    
  cohortLong <- eh$cohort |> 
    as_tibble() |> 
    rownames_to_column("ind") |> 
    mutate(ind = as.numeric(ind)) 
  
  summary_tt_z <- summary_tt_z0 |> 
    left_join(ehLong) |> 
    mutate(
      meanM1 = mean - 1,
      pSurv = ifelse(meanM1 == 0, 1, 1 - meanM1),
      enc8 = ifelse(enc == 1, 0.8, 0)
    ) |> 
    left_join(firstLong) |> 
    left_join(lastLong) |> 
    left_join(cohortLong)

Code

ojs_define(numTags_tt_OJS = dim(eh$tags)[1])
ojs_define(summary_tt_z_OJS = (summary_tt_z))

17.1.6 Plot the probability of survival (z) for selected individuals

Select one or more individuals :

Code

viewof selectInd_tt = Inputs.select([...Array(numTags_tt_OJS).keys()], {
  label: "Which fish?",
  value: 1,
  step: 1,
  multiple: true
})

summary_tt_z_OJS_T = transpose(summary_tt_z_OJS)
summary_tt_z_OJS_T_selected = summary_tt_z_OJS_T.filter((d) =>
  selectInd_tt.includes(d.ind)
)

Black dots/line is estimated probability of survival and orange dots are encountered yes (y = 0.8)/no (y = 0). The green line is the first observation and the red line is the last possible observation. The number in the upper right corner of each panel is the fish’s cohort.

Code

Plot.plot({
  width: width,
  height: 350,
  inset: 10,
  color: {
    scheme: "greys"
  },
  x: { label: "Age/season combination" },
  y: { label: "Probability of survival" },
  marks: [
    Plot.frame(),
    Plot.dot(summary_tt_z_OJS_T_selected, {
      x: "t",
      y: "pSurv"
    }),
    Plot.dot(summary_tt_z_OJS_T_selected, {
      x: "t",
      y: "enc8",
      fill: "orange"
    }),
    Plot.line(summary_tt_z_OJS_T_selected, {
      x: "t",
      y: "pSurv"
    }),
    Plot.ruleX(summary_tt_z_OJS_T_selected, {
      x: "first",
      y: 1,
      "stroke": "green"
    }),
    Plot.ruleX(summary_tt_z_OJS_T_selected, {
      x: "last",
      y: 1,
      "stroke": "red"
    })
    ,
    Plot.text(summary_tt_z_OJS_T_selected, 
      Plot.selectFirst({
        x: 11,
        y: 1,
        text: d => d.cohort
      })
    )
  ],
  facet: {
    data: summary_tt_z_OJS_T_selected,
    x: "ind"
  }
})

17.1.7 Output model summary data for Xiaowei

Code

  write.csv(summary_tt, './models/cmrNN_OB/tt/dataOut/xiaowei/summary_tt.csv')
  
  write.csv(summary_tt_z, './models/cmrNN_OB/tt/dataOut/xiaowei/summary_tt_z.csv')
  
  # #Output input data to Xiaowei directory. Turn to TRUE to output.
  # Output happens here: C:/Users/bletcher/OneDrive - DOI/projects/wbBook_quarto_targets/modelsCMR_NN_OB_outputData.qmd
  # if(FALSE) {
  # 
  #   for (i in seq_along(eh)){
  #     write.csv(eh[[i]], file = paste0('./models/cmrNN_OB/tt/dataOut/xiaowei/', 
  #                                      names(eh)[i], 
  #                                      ".csv"), 
  #               row.names = F)
  #   }
  # }

17.2 [s,s] model

Model with phi and p for each season_isYOY (YOY = young-of-year, isYOY = juvenile or adult) combination. Season is hierarchical over time. There are no data for the isYOY = TRUE, season = 2 state.

Probability of survival (phi) model structure:

phi[yoy, s] <- betaInt[yoy, s]

Probability of capture (p) model structure:

p[yoy, s] <- betaP[yoy, s]

Model code is in ./models/cmrNN_OB/ss/modelCMR_ss_NN_OB_functionsToSource.R
Model is run ‘by hand’ in ./models/cmrNN_OB/ss/modelCMR_ss_NN_OB_makeFile.R

Model runs:

17.2.1 Retrieve model results

Code

# Following https://oliviergimenez.github.io/bayesian-cr-workshop/worksheets/4_demo.html
#
  load('./models/cmrNN_OB/ss/runsOut/ss_NN_OB_mostRecent.RData')
  out_ss_NN_OB <- d

  #Input data
  eh <- tar_read(eh_OB_2002_2014_target)

17.2.2 Model code

In the model code, a value of 1 for z or in gamma or omega signifies the individual is alive and a value of 2 signifies the individual is dead. y[i,j] is the observed data (encounter history file). isYOY = 1 signifies YOY = TRUE (juvenile), and isYOY = 2 signifies YOY = FALSE (adult)

Code

out_ss_NN_OB$modelCode

{
    {
        delta[1] <- 1
        delta[2] <- 0
        for (i in 1:N) {
            for (t in first[i]:(last[i] - 1)) {
                gamma[1, 1, t, i] <- phi[i, t]
                gamma[1, 2, t, i] <- 1 - phi[i, t]
                gamma[2, 1, t, i] <- 0
                gamma[2, 2, t, i] <- 1
                omega[1, 1, t, i] <- 1 - p[i, t]
                omega[1, 2, t, i] <- p[i, t]
                omega[2, 1, t, i] <- 1
                omega[2, 2, t, i] <- 0
            }
        }
        for (i in 1:N) {
            for (t in first[i]:(last[i] - 1)) {
                logit(phi[i, t]) <- phiInt[isYOY_DATA[i, t], 
                  seasons[t]]
                logit(p[i, t]) <- pInt[isYOY_DATA[i, t], seasons[t]]
            }
        }
        for (yoy in 1:2) {
            for (s in 1:4) {
                phiInt[yoy, s] ~ T(dnorm(0, sd = 1/0.667), -3.5, 
                  3.5)
                pInt[yoy, s] ~ dunif(0, 1)
            }
        }
        for (i in 1:N) {
            z[i, first[i]] ~ dcat(delta[1:2])
            for (j in first[i]:(last[i] - 1)) {
                z[i, j + 1] ~ dcat(gamma[z[i, j], 1:2, j, i])
                yDATA[i, j + 1] ~ dcat(omega[z[i, j + 1], 1:2, 
                  j, i])
            }
        }
    }
}

17.2.3 Model statistics

Run statistics

nIter	nBurnin	nChains	thinRate
5000	2000	2	5

[1] “Run time = 38.594 mins”

17.2.4 Plot traces

Code

  priors <- runif(out_ss_NN_OB$runData$nIter * out_ss_NN_OB$runData$nChains, 0, 1)
  MCMCtrace(object = out_ss_NN_OB$mcmc,
            #ISB = FALSE,
            #exact = TRUE,
            params = c("phiInt", "pInt"),
            pdf = FALSE,
            priors = priors)

17.2.5 Summary data

Code

#|
  MCMCplot(object = out_ss_NN_OB$mcmc, params = c("phiInt"))

Code

  MCMCplot(object = out_ss_NN_OB$mcmc, params = c("pInt"))

Summary values

Code

#|
  (summary_ss <- MCMCsummary(object = out_ss_NN_OB$mcmc, params = c("phiInt", "pInt"), round = 3) %>%
    rownames_to_column(var = "var"))

            var   mean    sd   2.5%   50% 97.5% Rhat n.eff
1  phiInt[1, 1]  1.356 0.134  1.111 1.347 1.660 1.00   298
2  phiInt[2, 1]  1.204 0.197  0.851 1.191 1.645 1.01   282
3  phiInt[1, 2] -0.008 1.409 -2.749 0.006 2.660 1.00  1200
4  phiInt[2, 2]  0.299 0.073  0.162 0.297 0.441 1.05   327
5  phiInt[1, 3]  1.458 0.127  1.218 1.454 1.713 1.01   368
6  phiInt[2, 3]  0.586 0.098  0.412 0.583 0.795 1.05   363
7  phiInt[1, 4]  1.291 0.116  1.067 1.288 1.507 1.04   424
8  phiInt[2, 4]  0.737 0.133  0.497 0.730 1.005 1.01   298
9    pInt[1, 1]  0.773 0.093  0.585 0.774 0.958 1.02   402
10   pInt[2, 1]  0.929 0.060  0.785 0.945 0.997 1.04   591
11   pInt[1, 2]  0.498 0.290  0.031 0.484 0.974 1.00  1200
12   pInt[2, 2]  0.731 0.088  0.556 0.732 0.904 1.01   549
13   pInt[1, 3]  0.405 0.080  0.254 0.406 0.562 1.00   636
14   pInt[2, 3]  0.548 0.097  0.349 0.552 0.742 1.01   469
15   pInt[1, 4]  0.022 0.019  0.001 0.017 0.073 1.00   489
16   pInt[2, 4]  0.117 0.078  0.006 0.103 0.295 1.01   527

Code

  # To get the mMCMCSummaryRMNA funcion which I adapted to deal with NAs
  source('./models/cmrNN_OB/ss/modelCMR_ss_NN_OB_functionsToSource.R')

  summary_ss_z0 <- MCMCSummaryRMNA(object = out_ss_NN_OB$mcmc, params = c("z"), round = 3) %>%
    rownames_to_column(var = "var") |>
    mutate(
      ind0 = str_match(var, "([0-9]+), ([0-9]+)")[,2],
      t0 =   str_match(var, "([0-9]+), ([0-9]+)")[,3],
      ind = as.numeric(ind0),
      t = as.numeric(t0)
    ) |>
    dplyr::select(-c(t0, ind0))
  
  summary_ss_phi0 <- MCMCSummaryRMNA(object = out_ss_NN_OB$mcmc, params = c("phiInt"), round = 3) %>%
    rownames_to_column(var = "var") |>
    mutate(
      ind0 = str_match(var, "([0-9]+), ([0-9]+)")[,2],
      t0 =   str_match(var, "([0-9]+), ([0-9]+)")[,3],
      ind = as.numeric(ind0),
      t = as.numeric(t0)
    ) |>
    dplyr::select(-c(t0, ind0))
  
  summary_ss_p0 <- MCMCSummaryRMNA(object = out_ss_NN_OB$mcmc, params = c("pInt"), round = 3) %>%
    rownames_to_column(var = "var") |>
    mutate(
      ind0 = str_match(var, "([0-9]+), ([0-9]+)")[,2],
      t0 =   str_match(var, "([0-9]+), ([0-9]+)")[,3],
      ind = as.numeric(ind0),
      t = as.numeric(t0)
    ) |>
    dplyr::select(-c(t0, ind0))

Add other variables to summary values

Code

#|
  ehLong <-
    eh$eh |>
    as.data.frame() |>
    rownames_to_column("ind0") |>
    pivot_longer(starts_with("ais")) |>
    mutate(
      t = as.numeric(str_match(name, "([0-9]+)")[,1]),
      enc = value,
      ind = as.numeric(ind0)
    ) |>
    dplyr::select(-c(name, value, ind0))

  firstLong <- eh$first |>
    as_tibble() |>
    rownames_to_column("ind") |>
    mutate(ind = as.numeric(ind)) |>
    rename(first = value)

  lastLong <- eh$last |>
    as_tibble() |>
    rownames_to_column("ind") |>
    mutate(ind = as.numeric(ind)) |>
    rename(last = value)

  cohortLong <- eh$cohort |>
    as_tibble() |>
    rownames_to_column("ind") |>
    mutate(ind = as.numeric(ind))

  #### z
  summary_ss_z <- summary_ss_z0 |>
    left_join(ehLong) |>
    mutate(
      meanM1 = mean - 1,
      pSurv = ifelse(meanM1 == 0, 1, 1 - meanM1),
      enc8 = ifelse(enc == 1, 0.8, 0)
    ) |>
    left_join(firstLong) |>
    left_join(lastLong) |>
    left_join(cohortLong)
  
  ##### p
  summary_ss_p <- summary_ss_p0 |>
    left_join(ehLong) |>
    mutate(
      meanM1 = mean - 1,
      pSurv = ifelse(meanM1 == 0, 1, 1 - meanM1),
      enc8 = ifelse(enc == 1, 0.8, 0)
    ) |>
    left_join(firstLong) |>
    left_join(lastLong) |>
    left_join(cohortLong)
  
  #### phi
  summary_ss_phi <- summary_ss_phi0 |>
    left_join(ehLong) |>
    mutate(
      meanM1 = mean - 1,
      pSurv = ifelse(meanM1 == 0, 1, 1 - meanM1),
      enc8 = ifelse(enc == 1, 0.8, 0)
    ) |>
    left_join(firstLong) |>
    left_join(lastLong) |>
    left_join(cohortLong)

17.2.6 Retrieve Xiaowei results

Code

if(!exists("np")) {
  np <-import("numpy")
}

xP0 <- np$load("./data/in/fromXioawei/p_est.npy")

xP <- xP0 |> 
  as.data.frame() |> 
  rowid_to_column("ind") |> 
  pivot_longer(cols = starts_with("V")) |> 
  separate_wider_delim(name, delim = "V", names = c("d", "t")) |> 
  dplyr::select(-d) |> 
  mutate(t = as.numeric(t)) |> 
  rename(xP = value)


xPhi0 <- np$load("./data/in/fromXioawei/phi_est.npy")

xPhi <- xPhi0 |> 
  as.data.frame() |> 
  rowid_to_column("ind") |> 
  pivot_longer(cols = starts_with("V")) |> 
  separate_wider_delim(name, delim = "V", names = c("d", "t")) |> 
  dplyr::select(-d) |> 
  mutate(t = as.numeric(t))|> 
  rename(xPhi = value)

Plot Xiaowei’s results

Code

ggplot(xP, aes(t, xP)) +
  geom_point(alpha = 0.01)

Code

ggplot(xP, aes(t, xP)) +
  geom_jitter(alpha = 0.01)

Code

ggplot(xPhi, aes(t, xPhi)) +
  geom_jitter(alpha = 0.01)

17.2.7 Merge Xioawei results

Code

summary_xPhi <- xPhi |> 
  summarize(
    meanPhi = mean(xPhi),
    sdPhi = sd(xPhi)
  )

summary_xP <- xP |> 
  summarize(
    meanP = mean(xP),
    sdP = sd(xP)
  )

17.2.8 Merge Xioawei results

Code

summary_ss_phi_x <- summary_ss_phi0 |> 
  left_join(xPhi)

Observable interactivity

Code

ojs_define(numTags_ss_OJS = dim(eh$tags)[1])
ojs_define(summary_ss_z_OJS = (summary_ss_z))

ojs_define(summary_ss_phi_x_OJS = (summary_ss_phi_x))

17.2.9 Plot the probability of survival (z) for selected individuals

Select one or more individuals :

Code

viewof selectInd_ss = Inputs.select([...Array(numTags_ss_OJS).keys()], {
  label: "Which fish?",
  value: 1,
  step: 1,
  multiple: true
})

summary_ss_z_OJS_T = transpose(summary_ss_z_OJS)
summary_ss_z_OJS_T_selected = summary_ss_z_OJS_T.filter((d) =>
  selectInd_ss.includes(d.ind)
)

summary_ss_phi_x_OJS_T = transpose(summary_ss_phi_x_OJS)
summary_ss_phi_x_OJS_T_selected = summary_ss_phi_x_OJS_T.filter((d) =>
  selectInd_ss.includes(d.ind)
)

Black dots/line is estimated probability of being alive and orange dots are encountered yes (y = 0.8)/no (y = 0). The green line is the first observation and the red line is the last possible observation. The number in the upper right corner of each panel is the fish’s cohort.

Code

Plot.plot({
  width: width,
  height: 350,
  inset: 10,
  color: {
    scheme: "greys"
  },
  x: { label: "Age/season combination" },
  y: { label: "Probability of alive" },
  marks: [
    Plot.frame(),
    Plot.dot(summary_ss_z_OJS_T_selected, {
      x: "t",
      y: "pSurv"
    }),
    Plot.dot(summary_ss_z_OJS_T_selected, {
      x: "t",
      y: "enc8",
      fill: "orange"
    }),
    Plot.line(summary_ss_z_OJS_T_selected, {
      x: "t",
      y: "pSurv"
    }),
    Plot.ruleX(summary_ss_z_OJS_T_selected, {
      x: "first",
      y: 1,
      "stroke": "green"
    }),
    Plot.ruleX(summary_ss_z_OJS_T_selected, {
      x: "last",
      y: 1,
      "stroke": "red"
    })
    ,
    Plot.text(summary_ss_z_OJS_T_selected,
      Plot.selectFirst({
        x: 11,
        y: 1,
        text: d => d.cohort
      })
    )
  ],
  facet: {
    data: summary_ss_z_OJS_T_selected,
    x: "ind"
  }
})

17.2.10 Compare survival with Xioawei’s survival estimates

Code

Plot.plot({
  width: width,
  height: 350,
  inset: 10,
  color: {
    scheme: "greys"
  },
  x: { label: "Age/season combination" },
  y: { label: "Probability of survival" },
  marks: [
    Plot.frame(),
    Plot.dot(summary_ss_phi_x_OJS_T_selected, {
      x: "t",
      y: "mean"
    }),
    Plot.dot(summary_ss_phi_x_OJS_T_selected, {
      x: "t",
      y: "enc8",
      fill: "orange"
    }),
    Plot.line(summary_ss_phi_x_OJS_T_selected, {
      x: "t",
      y: "mean"
    }),
    
    Plot.dot(summary_ss_phi_x_OJS_T_selected, {
      x: "t",
      y: "xPhi"
    }),
    Plot.line(summary_ss_phi_x_OJS_T_selected, {
      x: "t",
      y: "xPhi"
    }),
    
    Plot.ruleX(summary_ss_phi_x_OJS_T_selected, {
      x: "first",
      y: 1,
      "stroke": "green"
    }),
    Plot.ruleX(summary_ss_phi_x_OJS_T_selected, {
      x: "last",
      y: 1,
      "stroke": "red"
    })
    ,
    Plot.text(summary_ss_phi_x_OJS_T_selected,
      Plot.selectFirst({
        x: 11,
        y: 1,
        text: d => d.cohort
      })
    )
  ],
  facet: {
    data: summary_ss_phi_x_OJS_T_selected,
    x: "ind"
  }
})

17.2.11 Output model summary data for Xiaowei

Code

  write.csv(summary_ss, './models/cmrNN_OB/ss/dataOut/xiaowei/summary_ss.csv')

  write.csv(summary_ss_z, './models/cmrNN_OB/ss/dataOut/xiaowei/summary_ss_z.csv')

  # #Output input data to Xiaowei directory. Turn to TRUE to output.
  # Output happens here: C:/Users/bletcher/OneDrive - DOI/projects/wbBook_quarto_targets/modelsCMR_NN_OB_outputData.qmd
  # if(FALSE) {
  #
  #   for (i in seq_along(eh)){
  #     write.csv(eh[[i]], file = paste0('./models/cmrNN_OB/tt/dataOut/xiaowei/',
  #                                      names(eh)[i],
  #                                      ".csv"),
  #               row.names = F)
  #   }
  # }